Name: Zimbabwe Social Contact Patterns
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Zimbabwe Social Contact Patterns

Households, Contacts, Time Use, and Participants

By [source]

About this dataset

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How to use the dataset

Start by examining the columns included in the dataset. Each column contains pertinent information about households and individuals participating in the study. Take note of any variables you may need for your analysis or questions you want to answer with the data (i.e., relationships among household members).

Investigate potential relationships between different columns – such as between type_house (type of house) and hhline (unique identifier for each household). This can help uncover correlations that may provide more insight into Zimbabwean social contact patterns within households.

Check out any missing values or areas of data that need to be cleaned up before analysis can begin – while looking out for potential bias points due to missing values or gaps in coverage caused by discrepancies when responding to survey questions over multiple days of questioning during study days 1 & 2
4 .Group variables together using sorting techniques such as clustering/classification or segmentation approaches; this lets researchers further break down overall trends into more manageable chunks which makes it easier to compare related samples/variables and improve accuracy when predicting outcomes from analyzing social contact networks at household levels across communities in Zimbabwe

Follow these steps when getting started with this detailed dataset from Zimbabwe’s Social Contact Patterns Study - which provides critical insights on how individual relationships shape community-level activities, structure geographic health risks, spread diseases - large scale assessment studies such as these significantly contribute towards evidence building for national level health interventions & policies looking at population health & wellbeing implications

Research Ideas

Examining the impact of different types of households on social contact patterns in Zimbabwe by looking at the relationship between the type of house, type of floor, access to amenities such as water, toilet and electricity and the number of participants related to a household.

Analyzing how gender affects social contacts in Zimbabwe by comparing the relationships of household members with an ego based on their gender and age.

Investigating how access to media (radio/televisions) influences time use patterns amongst participants in different areas with varying levels of access to these services across study sites, as well as any potential demographic differences in this area

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2017_Melegaro_Zimbabwe_hh_extra.csv

Column name	Description
hhline	Unique identifier for each household. (Integer)
study_site	Location of the study site. (String)
hhmem_relation_ego	Relationship of the household member to the ego. (String)
hhmem_age	Age of the household member. (Integer)
hhmem_gender	Gender of the household member. (String)
sleep_room_ego	Room in which the ego sleeps. (String)
hh_access_to_water	Access to water in the household. (String)
hh_toilet	Toilet facilities in the household. (String)
hh_shared_toilet	Whether the toilet is shared or not. (String)
hh_electricity	Access to electricity in the household. (String)
hh_radio	Access to a radio in the household. (String)
hh_television	Access to a television in the household. (String)
type_house	Type of house in which the household lives. (String)
type_floor	Type of floor in the house. (String)

File: 2017_Melegaro_Zimbabwe_contact_extra.csv

Column name	Description
studyDay	The day of the study. (Integer)
cnt_partrel	The number of participants in the study who reported a relationship with the ego. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

N 2017 Melegaro Zimbabwe Contact Common

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_contact_common

241.68 KB
26981 rows
15 columns


CREATE TABLE n_2017_melegaro_zimbabwe_contact_common (
  "cont_id" BIGINT,
  "part_id" BIGINT,
  "cnt_age_exact" DOUBLE,
  "cnt_age_est_min" DOUBLE,
  "cnt_age_est_max" DOUBLE,
  "cnt_gender" VARCHAR,
  "cnt_home" VARCHAR,
  "cnt_school" VARCHAR,
  "cnt_work" VARCHAR,
  "cnt_transport" VARCHAR,
  "cnt_leisure" VARCHAR,
  "cnt_otherplace" VARCHAR,
  "frequency_multi" VARCHAR,
  "phys_contact" DOUBLE,
  "duration_multi" VARCHAR
);

N 2017 Melegaro Zimbabwe Contact Extra

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_contact_extra

176.91 KB
26981 rows
3 columns


CREATE TABLE n_2017_melegaro_zimbabwe_contact_extra (
  "cont_id" BIGINT,
  "studyday" BIGINT,
  "cnt_partrel" VARCHAR
);

N 2017 Melegaro Zimbabwe Hh Common

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_hh_common

9.23 KB
1143 rows
3 columns


CREATE TABLE n_2017_melegaro_zimbabwe_hh_common (
  "hh_id" VARCHAR,
  "country" VARCHAR,
  "hh_size" BIGINT
);

N 2017 Melegaro Zimbabwe Hh Extra

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_hh_extra

66.88 KB
4728 rows
17 columns


CREATE TABLE n_2017_melegaro_zimbabwe_hh_extra (
  "hh_id" VARCHAR,
  "hhline" BIGINT,
  "hh_member_id" VARCHAR,
  "study_site" BIGINT,
  "hhmem_relation_ego" VARCHAR,
  "hhmem_age" DOUBLE,
  "hhmem_gender" VARCHAR,
  "sleep_room_ego" DOUBLE,
  "hh_access_to_water" DOUBLE,
  "hh_toilet" DOUBLE,
  "hh_shared_toilet" DOUBLE,
  "hh_electricity" DOUBLE,
  "hh_refridgerator" DOUBLE,
  "hh_radio" DOUBLE,
  "hh_television" DOUBLE,
  "type_house" DOUBLE,
  "type_floor" DOUBLE
);

N 2017 Melegaro Zimbabwe Household Common

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_household_common

9.23 KB
1143 rows
3 columns


CREATE TABLE n_2017_melegaro_zimbabwe_household_common (
  "hh_id" VARCHAR,
  "country" VARCHAR,
  "hh_size" BIGINT
);

N 2017 Melegaro Zimbabwe Household Extra

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_household_extra

40.81 KB
4728 rows
16 columns


CREATE TABLE n_2017_melegaro_zimbabwe_household_extra (
  "hh_id" VARCHAR,
  "hhline" BIGINT,
  "study_site" BIGINT,
  "hhmem_relation_ego" VARCHAR,
  "hhmem_age" DOUBLE,
  "hhmem_gender" VARCHAR,
  "sleep_room_ego" DOUBLE,
  "hh_access_to_water" VARCHAR,
  "hh_toilet" VARCHAR,
  "hh_shared_toilet" VARCHAR,
  "hh_electricity" VARCHAR,
  "hh_refridgerator" VARCHAR,
  "hh_radio" VARCHAR,
  "hh_television" VARCHAR,
  "type_house" VARCHAR,
  "type_floor" VARCHAR
);

N 2017 Melegaro Zimbabwe Participant Common

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_participant_common

17.73 KB
1245 rows
4 columns


CREATE TABLE n_2017_melegaro_zimbabwe_participant_common (
  "part_id" BIGINT,
  "hh_id" VARCHAR,
  "part_age" DOUBLE,
  "part_gender" VARCHAR
);

N 2017 Melegaro Zimbabwe Participant Extra

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_participant_extra

37.46 KB
1245 rows
27 columns


CREATE TABLE n_2017_melegaro_zimbabwe_participant_extra (
  "part_id" BIGINT,
  "type" DOUBLE,
  "study_site" BIGINT,
  "part_agegrp" BIGINT,
  "current_student" DOUBLE,
  "current_educ_lev" DOUBLE,
  "school_name" VARCHAR,
  "school_village" VARCHAR,
  "school_district" VARCHAR,
  "distance_school" DOUBLE,
  "trasp_school" DOUBLE,
  "school_size" DOUBLE,
  "class_size" DOUBLE,
  "ever_worked" DOUBLE,
  "work_sector" DOUBLE,
  "work_sector_detail" VARCHAR,
  "distance_work" DOUBLE,
  "transp_work" DOUBLE,
  "transp_work_detail" VARCHAR,
  "workplace_size" DOUBLE,
  "workplace_village" VARCHAR,
  "workplace_district" VARCHAR,
  "distance_tarred_road" DOUBLE,
  "bicycle" DOUBLE,
  "motorcycle" DOUBLE,
  "car" DOUBLE,
  "tractor" DOUBLE
);

N 2017 Melegaro Zimbabwe Sday

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_sday

20 KB
2490 rows
7 columns


CREATE TABLE n_2017_melegaro_zimbabwe_sday (
  "part_id" BIGINT,
  "sday_id" DOUBLE,
  "studyday" BIGINT,
  "day" DOUBLE,
  "month" DOUBLE,
  "year" DOUBLE,
  "dayofweek" BIGINT
);

N 2017 Melegaro Zimbabwe Time Use Common

@kaggle.thedevastator_zimbabwe_social_contact_patterns.n_2017_melegaro_zimbabwe_time_use_common

109.68 KB
2490 rows
107 columns


CREATE TABLE n_2017_melegaro_zimbabwe_time_use_common (
  "part_id" BIGINT,
  "studyday" BIGINT,
  "time_use_id" VARCHAR,
  "athome_0405" DOUBLE,
  "atschool_0405" DOUBLE,
  "atwork_0405" VARCHAR,
  "atshadowplace_0405" DOUBLE,
  "wvillage_0405" DOUBLE,
  "wward_0405" DOUBLE,
  "wdistrict_0405" DOUBLE,
  "odistrict_0405" DOUBLE,
  "athome_0506" DOUBLE,
  "atschool_0506" DOUBLE,
  "atwork_0506" DOUBLE,
  "atshadowplace_0506" DOUBLE,
  "wvillage_0506" DOUBLE,
  "wward_0506" DOUBLE,
  "wdistrict_0506" DOUBLE,
  "odistrict_0506" DOUBLE,
  "athome_0607" DOUBLE,
  "atschool_0607" DOUBLE,
  "atwork_0607" DOUBLE,
  "atshadowplace_0607" DOUBLE,
  "wvillage_0607" DOUBLE,
  "wward_0607" DOUBLE,
  "wdistrict_0607" DOUBLE,
  "odistrict_0607" DOUBLE,
  "athome_0710" DOUBLE,
  "atschool_0710" DOUBLE,
  "atwork_0710" DOUBLE,
  "atshadowplace_0710" DOUBLE,
  "wvillage_0710" DOUBLE,
  "wward_0710" DOUBLE,
  "wdistrict_0710" DOUBLE,
  "odistrict_0710" DOUBLE,
  "athome_1012" DOUBLE,
  "atschool_1012" DOUBLE,
  "atwork_1012" DOUBLE,
  "atshadowplace_1012" DOUBLE,
  "wvillage_1012" DOUBLE,
  "wward_1012" DOUBLE,
  "wdistrict_1012" DOUBLE,
  "odistrict_1012" DOUBLE,
  "athome_1214" DOUBLE,
  "atschool_1214" DOUBLE,
  "atwork_1214" DOUBLE,
  "atshadowplace_1214" DOUBLE,
  "wvillage_1214" DOUBLE,
  "wward_1214" DOUBLE,
  "wdistrict_1214" DOUBLE,
  "odistrict_1214" DOUBLE,
  "athome_1416" DOUBLE,
  "atschool_1416" DOUBLE,
  "atwork_1416" DOUBLE,
  "atshadowplace_1416" DOUBLE,
  "wvillage_1416" DOUBLE,
  "wward_1416" DOUBLE,
  "wdistrict_1416" DOUBLE,
  "odistrict_1416" DOUBLE,
  "athome_1617" DOUBLE,
  "atschool_1617" DOUBLE,
  "atwork_1617" DOUBLE,
  "atshadowplace_1617" DOUBLE,
  "wvillage_1617" DOUBLE,
  "wward_1617" DOUBLE,
  "wdistrict_1617" DOUBLE,
  "odistrict_1617" DOUBLE,
  "athome_1718" DOUBLE,
  "atschool_1718" DOUBLE,
  "atwork_1718" DOUBLE,
  "atshadowplace_1718" DOUBLE,
  "wvillage_1718" DOUBLE,
  "wward_1718" DOUBLE,
  "wdistrict_1718" DOUBLE,
  "odistrict_1718" DOUBLE,
  "athome_1819" DOUBLE,
  "atschool_1819" DOUBLE,
  "atwork_1819" DOUBLE,
  "atshadowplace_1819" DOUBLE,
  "wvillage_1819" DOUBLE,
  "wward_1819" DOUBLE,
  "wdistrict_1819" DOUBLE,
  "odistrict_1819" DOUBLE,
  "athome_1920" DOUBLE,
  "atschool_1920" DOUBLE,
  "atwork_1920" DOUBLE,
  "atshadowplace_1920" DOUBLE,
  "wvillage_1920" DOUBLE,
  "wward_1920" DOUBLE,
  "wdistrict_1920" DOUBLE,
  "odistrict_1920" DOUBLE,
  "athome_2000" DOUBLE,
  "atschool_2000" DOUBLE,
  "atwork_2000" DOUBLE,
  "atshadowplace_2000" DOUBLE,
  "wvillage_2000" DOUBLE,
  "wward_2000" DOUBLE,
  "wdistrict_2000" DOUBLE,
  "odistrict_2000" DOUBLE,
  "athome_0004" DOUBLE
);